GHSA-7JVW-F348-84GQ

Vulnerability from github – Published: 2026-09-30 18:33 – Updated: 2026-10-01 03:31
VLAI
Details

A remote, unauthenticated TLS client can make a server crash or call through a freed pointer if its sni_callback assigns a different context to SSLSocket.context (the documented way to select a certificate per server name) and nothing else keeps the original ssl.SSLContext alive. Typical cases are servers that create an SSLContext per connection or replace it while connections are open; servers that wrap their listening socket with it are not affected.

Mitigation: keep a reference to every SSLContext that sets sni_callback for the lifetime of the server. TLS clients are not affected.

Severity
9.2 (Critical)
CVSS:4.0/AV:N/AC:H/AT:P/PR:N/UI:N/VC:H/VI:H/VA:L/SC:N/SI:N/SA:N
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{
  "affected": [],
  "aliases": [
    "CVE-2026-19445"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-416"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2026-09-30T17:16:45Z",
    "severity": "CRITICAL"
  },
  "details": "A remote, unauthenticated TLS client can make a server crash or call\nthrough a freed pointer if its sni_callback assigns a different context to\nSSLSocket.context (the documented way to select a certificate per server\nname) and nothing else keeps the original ssl.SSLContext alive. Typical\ncases are servers that create an SSLContext per connection or replace it\nwhile connections are open; servers that wrap their listening socket with\nit are not affected.\n\n\nMitigation: keep a reference to every SSLContext that sets sni_callback for\nthe lifetime of the server. TLS clients are not affected.",
  "id": "GHSA-7jvw-f348-84gq",
  "modified": "2026-10-01T03:31:11Z",
  "published": "2026-09-30T18:33:49Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-19445"
    },
    {
      "type": "WEB",
      "url": "https://github.com/python/cpython/issues/156293"
    },
    {
      "type": "WEB",
      "url": "https://github.com/python/cpython/pull/158504"
    },
    {
      "type": "WEB",
      "url": "https://github.com/python/cpython/commit/34a53dce8174da2fceb12fe084a4def02a10053d"
    },
    {
      "type": "WEB",
      "url": "https://github.com/python/cpython/commit/46133cd57d309652139ada74014aca7665ac552b"
    },
    {
      "type": "WEB",
      "url": "https://github.com/python/cpython/commit/63fab143d94cafae71850831acfb52041ba44af7"
    },
    {
      "type": "WEB",
      "url": "https://github.com/python/cpython/commit/b12968cefe69ca1dcb8606c832ff73ee7dbf4ba8"
    },
    {
      "type": "WEB",
      "url": "https://github.com/python/cpython/commit/cd7e51e7d4563866fbaa1e2521ae69b45daf3698"
    },
    {
      "type": "WEB",
      "url": "https://github.com/python/cpython/commit/d8717ed01717a9641686e6e6f83f0ab8af235e2c"
    },
    {
      "type": "WEB",
      "url": "https://github.com/python/cpython/commit/ec44b5a3258cbda947d5e07242ee562ed05ef24b"
    },
    {
      "type": "WEB",
      "url": "https://mail.python.org/archives/list/security-announce@python.org/thread/QMQIUQB6WGGC3MI7I3WKQXOYOBDSPPS3"
    },
    {
      "type": "WEB",
      "url": "http://www.openwall.com/lists/oss-security/2026/09/30/17"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:4.0/AV:N/AC:H/AT:P/PR:N/UI:N/VC:H/VI:H/VA:L/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X",
      "type": "CVSS_V4"
    }
  ]
}



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Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.

Sightings

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  • Confirmed: The vulnerability has been validated from an analyst's perspective.
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  • Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.



Related by attack behaviour

Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.